The present invention relates to systems and methods that aid in providing a medical diagnosis or risk assessment for a patient using biochemical and historic patient data, including data from point of care diagnostic tests or assays, and processing the information to give an indication of a medical condition or risk.
Evaluation of Immunoassay Data
In diagnostic immunochromatographic assays, where results are determined by a color change or the production of color, results are generally detected visually by human eye. As a result of the human perception and judgment involved, there is significant variance among those interpreting such test results as to whether a color change or other measurable signal has occurred, and the degree of such occurrence. Furthermore, there is a great deal of subjectivity involved in interpreting whether immunoassay results are positive or negative. This is particularly pronounced where the result is close to a threshold value. The variance is further enhanced when attempts are made to quantitate such assay test results. Accurate results may be critical for certain diagnostic assays.
It is desirable to develop techniques that are objective in nature, and that reduce the error associated with interpreting immunochromato-graphic and other assay test results. Therefore, it is an object herein to provide systems, methods, devices and instruments for objectively assessing data from biochemical and other tests and to use such data for diagnosis and risk assessment. It is also an object herein to incorporate decision-support methodologies into such systems and thereby enhance the diagnostic and risk assessment capabilities thereof.
It is also an object herein to provide systems and methods for use in detecting and measuring fetal fibronectin (fFN) levels in a patient sample and using such information to diagnose and assess risks of preterm labor, fetal membrane rupture and other related disorders and conditions.
Systems and methods for medical diagnosis or risk assessment for a patient are provided. These systems and methods are designed to be employed at the point of care, such as in emergency rooms, operating rooms, hospital laboratories and other clinical laboratories, doctor""s offices, in the field, or in any situation in which a rapid and accurate result is desired. The systems and methods process patient data, particularly data from point of care diagnostic tests or assays, including immunoassays, chemical assays, nucleic acid assays, colorimetric assays, fluorometric assays, chemiluminescent and bioluminescent assays, electrocardiograms, X-rays and other such tests, and provide an indication of a medical condition or risk or absence thereof.
The systems include an instrument for reading or evaluating the test data and software for converting the data into diagnostic or risk assessment information. In certain embodiments, the systems include a test device, such as a test strip, optionally encased in a housing, for analyzing patient samples and obtaining patient data. In particular embodiments, the device includes a symbology, such as a bar code, which is used to associate identifying information, such as intensity value, standard curves, patient information, reagent information and other such information, with the test device. The reader in the system is optionally adapted to read the symbology.
Further, the systems optionally include a decision-support system or systems, such as a neural network, for evaluating the digitized data, and also for subsequent assessment of the data, such as by integration with other patient information, including documents and information in medical records. All software and instrument components are preferably included in a single package. Alternatively, the software can be contained in a remote computer so that the test data obtained at a point of care can be sent electronically to a processing center for evaluation. Thus, the systems operate on site at the point of care, such as in a doctor""s office, or remote therefrom.
The patient information includes data from physical and biochemical tests, such as immunoassays, and from other procedures. The test is performed on a patient at the point of care and generates data that can be digitized, such as by an electronic reflectance or transmission reader, which generates a data signal. The signal is processed using software employing data reduction and curve fitting algorithms, or a decision support system, such as a trained neural network, or combinations thereof, for converting the signal into data, which is used to aid in diagnosis of a medical condition or determination of a risk of disease. This result may be further entered into a second decision support system, such as a neural net, for refinement or enhancement of the assessment.
In a particular embodiment, systems and methods for detecting and measuring levels of a target analyte in a patient sample, analyzing the resulting data, and providing a diagnosis or risk assessment are provided. The systems and methods include an assay device in combination with a reader, particularly a computer-assisted reader, preferably a reflectance reader, and data processing software employing data reduction and curve fitting algorithms, optionally in combination with a trained neural network for accurately determining the presence or concentration of analyte in a biological sample. The methods include the steps of performing an assay on a patient sample, reading the data using a reflectance reader and processing the reflectance data using data processing software employing data reduction algorithms. In a particular embodiment, the assay is an immunoassay. Preferred software includes curve fitting algorithms, optionally in combination with a trained neural network, to determine the presence or amount of analyte in a given sample. The data obtained from the reader then can be further processed by the medical diagnosis system to provide a risk assessment or diagnosis of a medical condition as output. In alternative embodiments, the output can be used as input into a subsequent decision support system, such as a neural network, that is trained to evaluate such data.
In a preferred embodiment, the assay device is a lateral flow test strip, preferably, though not necessarily, encased in a housing, designed to be read by the reader, and the assay is a sandwich immunoassay. For example, in one embodiment thereof, a patient sample is contacted with an antibody for a selected target analyte indicative of a disease, disorder or risk thereof. The antibody is preferably labeled by conjugation to a physically detectable label, and upon contacting with the sample containing the target analyte forms a complex. The antibody-analyte complex is then contacted with a second antibody for the antigen, which is immobilized on a solid support. The second antibody captures the antibody-analyte complex to form an antibody-analyte-antibody sandwich complex, and the resulting complex, which is immobilized on the solid support, is detectable by virtue of the label. The test strip is then inserted into a reader, where the signal from the label in the complex is measured. Alternatively, the test strip could be inserted into the reader prior to addition of the sample. Additionally, the housing may include a symbology, such as a bar code, which is also read by the reader and contains data related to the assay device and/or test run. The signal obtained is processed using data processing software employing data reduction and curve fitting algorithms, optionally in combination with a trained neural network, to give either a positive or negative result, or a quantitative determination of the concentration of analyte in the sample, which is correlated with a result indicative of a risk or presence of a disease or disorder. This result can optionally be input into a decision support system, and processed to provide an enhanced assessment of the risk of a medical condition as output. The entire procedure may be automated and/or computer-controlled.
In certain embodiments, the reflectance reader is adapted to read a symbology on the test device. The symbology is preferably a bar code, which be read in the same manner that the test strip in the device can be read. In these embodiments, the reader head scans across a bar code in a stepwise fashion. The data collected from the bar code is transformed into integrated peak information and analyzed as alphanumeric characters, which are related to information related to the particular device and/or test run or other information, including patient information. Any bar code from among the many known in the in industry. In preferred embodiments, Code 39 (a trademark of Interface Mechanism, Inc., Lynnwood, WA; see, e.g., U.S. Pat. Nos. 4,379,224, 4,438,327, 4,511,259 or Code 128 bar codes (see, e.g., U.S. Pat. No. 5,227,893) are used.
In a particular embodiment, the analyte to be detected is fetal fibronectin (fFN) and the result obtained is a positive or negative indication of pregnancy or the risk of certain pregnancy-related conditions or fertility and infertility-related conditions, including ectopic pregnancy, preterm labor, pre-eclampsia, imminent delivery, term induction and fetal membrane rupture. Thus, provided herein is a rapid fFN test using a lateral flow test device.
At the very least, this test provides the same clinically relevant information as a fFN ELISA (an enzyme linked immunosorbent sandwich assay (ELISA)) test heretofore available in significantly less time and at the point of care. The fFN immunoassay provided herein allows the user to test a cervicovaginal swab sample in about 20 minutes. When practiced as described herein, additional information, such as a more accurate risk assessment or diagnosis, can be obtained.
The system herein provides a means to detect and to quantitate concentrations of fFN throughout pregnancy and to assess the risk and detect conditions associated therewith. Because of the sensitivity of the combination of the reader and devices provided herein, fFN may be monitored throughout pregnancy, including times when it is not detected by less sensitive systems.
The reflectance reader and test strip device are also provided herein. Also provided herein are the neural nets for assessing the data.
A method for classifying an image is also provided. The method includes the steps of reducing the image to a set of derived parameters from which the image can be reconstructed within a predetermined degree of tolerance; inputting the derived parameters into a classification neural network; and determining the classification of the image based on the output of the classification neural network. The method of reducing the image to a set of derived parameters is achieved by defining a mathematical function that contains a plurality of parameters representative of the image; and optimizing the parameters of the function using a methodology that minimizes the error between the image and a reconstruction of the image using the function.
In an alternative embodiment, the method of reducing the image to a set of derived parameters is achieved by inputting the image into a trained neural network, where the inputs to the network represent the image, the hidden layer of the network is such that the number of hidden elements is smaller than the number of inputs to the network, and the outputs of the network represent reconstruction of the image; and setting the derived parameters to the output values of the trained neural network.
In another alternative embodiment, the method of reducing the image to a set of derived parameters is achieved by defining a neural network in which the inputs to the network are the coordinates of a point in the image, the hidden layer contains a plurality of elements, and the output of the network represents the reconstruction of the associated point in the image; training the neural network so that the error between the network output and the image are minimized for all points in the image; and setting the derived parameters to the weights of the hidden layer of the trained neural network.
The neural networks and computer systems used in the methods are also provided.